"""Transform a roidb into a trainable roidb by adding a bunch of metadata."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import datasets
import numpy as np
from model.utils.config import cfg
from datasets.factory import get_imdb
import PIL
import pdb

def prepare_roidb(imdb):
  """Enrich the imdb's roidb by adding some derived quantities that
  are useful for training. This function precomputes the maximum
  overlap, taken over ground-truth boxes, between each ROI and
  each ground-truth box. The class with maximum overlap is also
  recorded.
  """

  roidb = imdb.roidb
  if not (imdb.name.startswith('coco')):
    widths = imdb.widths
    heights = imdb.heights

  #xmax,ymax,xmin,ymin = (0,0,300,300)
         
  for i in range(len(imdb.image_index)):
    #if (np.max(roidb[i]['boxes'][:,::2]) > xmax):
     # xmax = np.max(roidb[i]['boxes'][:,::2])
    #if (np.max(roidb[i]['boxes'][:,1::2]) > ymax):
      #ymax = np.max(roidb[i]['boxes'][:,1::2])
    #if (np.min(roidb[i]['boxes'][:,::2]) < xmin):
      #xmin = np.min(roidb[i]['boxes'][:,::2])
    #if (np.min(roidb[i]['boxes'][:,1::2]) < ymin):
      #ymin = np.min(roidb[i]['boxes'][:,1::2])

    roidb[i]['img_id'] = imdb.image_id_at(i)
    roidb[i]['image'] = imdb.image_path_at(i)
    if not (imdb.name.startswith('coco')):
      roidb[i]['width'] = widths[i]
      roidb[i]['height'] = heights[i]
    # need gt_overlaps as a dense array for argmax
    if 'gt_overlaps' in roidb[i]:
      gt_overlaps = roidb[i]['gt_overlaps'].toarray()
      # max overlap with gt over classes (columns)
      max_overlaps = gt_overlaps.max(axis=1)
      # gt class that had the max overlap
      max_classes = gt_overlaps.argmax(axis=1)
      roidb[i]['max_classes'] = max_classes
      roidb[i]['max_overlaps'] = max_overlaps
      # sanity checks
      # max overlap of 0 => class should be zero (background)
      zero_inds = np.where(max_overlaps == 0)[0]
      assert all(max_classes[zero_inds] == 0)
      # max overlap > 0 => class should not be zero (must be a fg class)
      nonzero_inds = np.where(max_overlaps > 0)[0]
      assert all(max_classes[nonzero_inds] != 0)

def rank_roidb_ratio(roidb):
    # rank roidb based on the ratio between width and height.
    ratio_large = 2 # largest ratio to preserve.
    ratio_small = 0.5 # smallest ratio to preserve.    
    
    ratio_list = []
    for i in range(len(roidb)):
      width = roidb[i]['width']
      height = roidb[i]['height']
      ratio = width / float(height)

      if ratio > ratio_large:
        roidb[i]['need_crop'] = 1
        ratio = ratio_large
      elif ratio < ratio_small:
        roidb[i]['need_crop'] = 1
        ratio = ratio_small        
      else:
        roidb[i]['need_crop'] = 0

      ratio_list.append(ratio)

    ratio_list = np.array(ratio_list)
    ratio_index = np.argsort(ratio_list)
    return ratio_list[ratio_index], ratio_index

def filter_roidb(roidb):
    # filter the image without bounding box.
    print('before filtering, there are %d images...' % (len(roidb)))
    i = -1

    while i < len(roidb) -1 :
      i += 1
      if 'boxes' in roidb[i]:
        if len(roidb[i]['boxes']) == 0:
          del roidb[i]
          i -= 1
          continue

      if 'grasps' in roidb[i]:
        if len(roidb[i]['grasps']) == 0:
          del roidb[i]
          i -= 1

    print('after filtering, there are %d images...' % (len(roidb)))
    return roidb

def combined_roidb(imdb_names, training=True):
  """
  Combine multiple roidbs
  """

  def get_training_roidb(imdb):
    """Returns a roidb (Region of Interest database) for use in training."""
    print('Preparing training data...')

    prepare_roidb(imdb)
    #ratio_index = rank_roidb_ratio(imdb)
    print('done')

    return imdb.roidb
  
  def get_roidb(imdb_name):
    imdb = get_imdb(imdb_name)
    print('Loaded dataset `{:s}` for training'.format(imdb.name))
    imdb.set_proposal_method(cfg.TRAIN.COMMON.PROPOSAL_METHOD)
    print('Set proposal method: {:s}'.format(cfg.TRAIN.COMMON.PROPOSAL_METHOD))
    roidb = get_training_roidb(imdb)
    return roidb

  roidbs = [get_roidb(s) for s in imdb_names.split('+')]
  roidb = roidbs[0]

  if len(roidbs) > 1:
    for r in roidbs[1:]:
      roidb.extend(r)
    tmp = get_imdb(imdb_names.split('+')[1])
    imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
  else:
    imdb = get_imdb(imdb_names)

  if training:
    roidb = filter_roidb(roidb)

  ratio_list, ratio_index = rank_roidb_ratio(roidb)

  return imdb, roidb, ratio_list, ratio_index
